Introduction to Sociology/Sociological Methods - Wikibooks, open books for an open world
Even if there are causal relationships to discover, we have difficulty in This kind of imprecise, 'probabilistic' association is typical of sociological findings. An anthropologist would not be your first choice for statistical ad- vice on the applicability . The dilemma of culture is that, unlike those causal relationships in which an .. "A note on fertility among the Tallensi of the Gold Coast," Sociological. The truth of counterfactuals is closely related to the existence of causal relationships. The counterfactual claim made above implies that there is a causal link.
And most fundamentally, she argues that identifying causal relations requires substantive theories of the causal powers capacities, in her language that govern the entities in question.
Understanding Society: Causal diagrams and causal mechanisms
Causal relations cannot be directly inferred from facts about association among variables. The importance of this idea for sociological research is profound; it confirms the notion shared by many researchers that attribution of social causation depends inherently on the formulation of good, middle-level theories about the real causal properties of various social forces and entities.
What is a causal mechanism? A causal relation exists between X and Y if and only if there is a set of causal mechanisms that connect X to Y.
Introduction to Sociology/Sociological Methods
This is an ontological premise, asserting that causal mechanisms are real and are the legitimate object of scientific investigation. He argues that sociology requires better integration of theory and evidence. Central to an adequate explanatory theory, however, is the specification of the mechanism that is hypothesized to underlie a given set of observations.
Rather, it is necessary to have a hypothesis of the mechanisms that link the variables before we can arrive at a justified estimate of the relative importance of the causal variables in bringing about the outcome.
The general nature of the mechanisms that underlie sociological causation has been very much the subject of debate. Two broad approaches may be identified: The former follow the strategy of aggregating the results of individual-level choices into macro-level outcomes; the latter attempt to identify the factors that work behind the backs of agents to influence their choices.
Jon Elster has also shed light on the ways in which the tools of rational choice theory support the construction of largescale sociological explanations The Cement of Society: Non-parametric here means simply that we do not assume that the data are distributed normally.
Elwert credits the development of the logic of DAGs to Judea Pearl and Peter Spirtes, along with other researchers within the causal modeling community. A crucial feature of DAGitty is that it is not solely a graphical program for drawing graphs of possible causal relationships; rather, it embodies an underlying logic which generates expected statistical relationships among variables given the stipulated relationships on the graph.
Here is a screenshot from the platform: The question to consider here is whether there is a relationship between the methodology of causal mechanisms and the causal theory reflected in these causal diagrams.
It is apparent that the underlying ontological assumptions associated with the two approaches are quite different. Causal mechanisms theory is generally associated with a realist approach to the social world, and generally rejects the Humean theory of causation.
The causal diagram approach, by contrast, is premised on the Humean and statistical approach to causation. A causal mechanisms hypothesis is not fundamentally evaluated in terms of the statistical relationships among a set of variables; whereas a standard causal model is wholly intertwined with the mathematics of conditional correlation.
Consider a few examples. Here is a complex graphical representation of a process understood in terms of causal mechanisms from McGinnes and Elandy, "Unintended Behavioural Consequences of Publishing Performance Data: Is More Always Better? Plainly this model is impossible to evaluate statistically by attempting to measure each of the variables; instead, the researchers proceed by validating the individual mechanisms identified here as well as the direction of influence they have on other intermediate outcomes.
The outcome of interest is "quality of learning" at the center of the graph; and the diagram attempts to represent the complex structure of causal influences that exist among several dozen mechanisms or causal factors.
Here is another example of a causal mechanisms path diagram, this time representing the causal system involved in drought and mental health by Vins, Bell, Saha, and Hess link.
Here too the model is not offered as a statistical representation of covariance among variables; rather, it is a hypothetical sketch of the factors which play in mechanisms leading from drought to depression and anxiety in a population. And the assessment of the model should not take the form of a statistical evaluation a non-parametric structural equation modelbut rather a piecemeal verification of the validity of the specific mechanisms cited.
John Gerring argues that this is a major weakness in causal mechanisms theory, however, in "Causal Mechanisms? It seems, therefore, that the superficial similarity between a causal model graph a DAG and a causal mechanisms diagram is only skin-deep.
Fundamentally the two approaches make very different assumptions about both ontology what a causal relationship is and epistemology how we should empirically evaluate a causal claim. So it seems unlikely that it will be fruitful for causal-mechanisms theorists to attempt to adapt methods like DAGs to represent the causal claims they want to advance and evaluate.How Ice Cream Kills! Correlation vs. Causation